title: Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey.
abstract: BACKGROUND
  Modelling is widely used to inform decisions about management of malaria and acute febrile illnesses. Most models depend on estimates of the probability that untreated patients with malaria or bacterial illnesses will progress to severe disease or death. However, data on these key parameters are lacking and assumptions are frequently made based on expert opinion. Widely diverse opinions can lead to conflicting outcomes in models they inform.
  
  
  RESULTS
  A Delphi survey was conducted with malaria experts aiming to reach consensus on key parameters for public health and economic models, relating to the outcome of untreated febrile illnesses. Survey questions were stratified by malaria transmission intensity, patient age, and HIV prevalence. The impact of the variability in opinion on decision models is illustrated with a model previously used to assess the cost-effectiveness of malaria rapid diagnostic tests. Some consensus was reached around the probability that patients from higher transmission settings with untreated malaria would progress to severe disease (median 3%, inter-quartile range (IQR) 1-5%), and the probability that a non-malaria illness required antibiotics in areas of low HIV prevalence (median 20%). Children living in low transmission areas were considered to be at higher risk of progressing to severe malaria (median 30%, IQR 10-58%) than those from higher transmission areas (median 13%, IQR 7-30%). Estimates of the probability of dying from severe malaria were high in all settings (medians 60-73%). However, opinions varied widely for most parameters, and did not converge on resurveying.
  
  
  CONCLUSIONS
  This study highlights the uncertainty around potential consequences of untreated malaria and bacterial illnesses. The lack of consensus on most parameters, the wide range of estimates, and the impact of variability in estimates on model outputs, demonstrate the importance of sensitivity analysis for decision models employing expert opinion. Results of such models should be interpreted cautiously. The diversity of expert opinion should be recognised when policy options are debated.
authors.affiliation: London School of Hygiene & Tropical Medicine, London, United Kingdom. yoel@tropmedres.ac
authors.forename: Yoel
authors.initials: Y
authors.lastname: Lubell
authors.forename: Sarah G
authors.initials: SG
authors.lastname: Staedke
authors.forename: Brian M
authors.initials: BM
authors.lastname: Greenwood
authors.forename: Moses R
authors.initials: MR
authors.lastname: Kamya
authors.forename: Malcolm
authors.initials: M
authors.lastname: Molyneux
authors.forename: Paul N
authors.initials: PN
authors.lastname: Newton
authors.forename: Hugh
authors.initials: H
authors.lastname: Reyburn
authors.forename: Robert W
authors.initials: RW
authors.lastname: Snow
authors.forename: Umberto
authors.initials: U
authors.lastname: D'Alessandro
authors.forename: Mike
authors.initials: M
authors.lastname: English
authors.forename: Nick
authors.initials: N
authors.lastname: Day
authors.forename: Peter
authors.initials: P
authors.lastname: Kremsner
authors.forename: Arjen
authors.initials: A
authors.lastname: Dondorp
authors.forename: Wilfred
authors.initials: W
authors.lastname: Mbacham
authors.forename: Grant
authors.initials: G
authors.lastname: Dorsey
authors.forename: Seth
authors.initials: S
authors.lastname: Owusu-Agyei
authors.forename: Kathryn
authors.initials: K
authors.lastname: Maitland
authors.forename: Sanjeev
authors.initials: S
authors.lastname: Krishna
authors.forename: Charles
authors.initials: C
authors.lastname: Newton
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Pasvol
authors.forename: Terrie
authors.initials: T
authors.lastname: Taylor
authors.forename: Lorenz
authors.initials: L
authors.lastname: von Seidlein
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: White
authors.forename: Fred
authors.initials: F
authors.lastname: Binka
authors.forename: Anne
authors.initials: A
authors.lastname: Mills
authors.forename: Christopher J M
authors.initials: CJ
authors.lastname: Whitty
mesh_terms: D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D016638:Critical Illness / Q000191:economics / Q000453:epidemiology / Q000628:therapy; D003661:Decision Support Techniques; D003697:Delphi Technique; D018450:Disease Progression; D005334:Fever / Q000150:complications / Q000175:diagnosis* / Q000191:economics* / Q000453:epidemiology; D006262:Health; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008288:Malaria / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology; D018803:Models, Economic; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D011336:Probability; D011379:Prognosis; D014329:Tropical Climate; D055815:Young Adult